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Duration 14 hours
Course Outline
Introduction to the Huawei AI Ecosystem
- Overview of Ascend AI hardware, including the 310, 910, and 910B series
- Key upper-layer components: MindSpore, CANN, and AscendCL
- Market positioning and core architectural principles
The Function of CANN within Huawei’s AI Stack
- Defining CANN: The SDK’s objectives and internal stratification
- ATC, TBE, and AscendCL: Processes for model compilation and execution
- How CANN drives inference optimization and deployment efficiency
MindSpore: Overview and Structural Design
- Training and inference workflows within the MindSpore environment
- Graph mode, PyNative, and the mechanics of hardware abstraction
- Connectivity with Ascend NPUs through the CANN backend
Managing the AI Lifecycle on Ascend: From Training to Rollout
- Creating models in MindSpore or converting them from external frameworks
- Exporting and compiling models via ATC
- Deploying OM models on Ascend hardware using AscendCL
Benchmarking Against Competing AI Stacks
- MindSpore versus PyTorch and TensorFlow: Strategic focus and market positioning
- Deployment methodologies on Ascend compared to GPU-centric architectures
- Evaluating opportunities and constraints for enterprise applications
Enterprise Integration Use Cases
- Applications in smart manufacturing, public sector AI, and telecommunications
- Factors affecting scalability, regulatory compliance, and ecosystem health
- Implementing hybrid cloud/on-prem deployments using the Huawei stack
Conclusion and Future Directions
Requirements
- General knowledge of AI workflows or platform design principles
- Foundational understanding of model training and deployment processes
- No previous practical experience with CANN or MindSpore is necessary
Target Audience
- AI platform assessors and infrastructure architects
- AI/ML DevOps engineers and pipeline integration specialists
- Technology leaders and strategic decision-makers
